E-Learning Readiness Assessment Using Machine Learning Methods

Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.

Teaching Practices Impact on the Level of Contentedness for Electrical Engineering Students. A Longitudinal Study

Results of a longitudinal research carried out within the Faculty of Electrical Engineering (University POLITEHNICA of Bucharest) to identify the dynamics of student preferences regarding the teaching-learning-assessment process are presented in this paper. The research was carried out throughout one full generation (four academic years) of students. The results showed that the academic maturity of the students (defined as the transition to a higher academic year) majorly impacts only the students' preferences regarding some aspects like the way of conducting the laboratory and project applications, the subject's final evaluation procedure, the fining of academic deception and the mandatory evaluation of professors' activity by students. The studied generation (2016-2019) is the last one before the COVID-19 pandemic, before the paradigm shifts through the sudden transition to fully online activities highlighting the relevance of this research. © 2023 IEEE.

Comparison on the performance of two qPCR instruments in the detection of SARS-CoV-2 virus

Objective To compare the performance of two qPCR instruments in detecting SARS-CoV-2 virus in the nasopharyngeal swab samples of suspected COVID-19 isolated individuals in Jinghu district Wuhu city.Methods A total of 151 nasopharyngeal swab samples were collected from individuals with suspected COVID-19isolated during January 2021 and July 2022 at a quarantine site in the Jinghu district. Nucleic acid of SARS-CoV-2virus was quantified parallelly using ABIQ5 real-time fluorescence quantitative analyzer（Q5 analyzer） and Bole CFX96 fluorescence quantitative PCR analyzer（Bole analyzer） in the laboratory. Q5 analyzer was used as the reference instrument, while Bole analyzer was used as an experimental instrument. The detection results of N gene, ORF1ab fragment and CT value of the two RT-PCR machines were analyzed and compared using paired four grid test, Spearman test and paired sample t-test in SPSS 22 statistical software. Results The results of 151samples for different target genes tested by two instruments were in good agreement（N gene: Kappa=1, P＜0. 05;ORF1ab fragment: Kappa=0. 972, P＜0. 05）. The inter-batch repeatability rates were 4. 01% and 3. 04%for N gene and ORF fragment of the same batch positive quality controls by Q5 analyzer, and were 4. 90% and 3. 57% by Bole analyzer. The intra batch repeatability rates of the two instruments at different hole locations were similar, and CV values were less than 3%. The results of 23 positive samples showed that the differences in CT values of N gene（29. 38±7. 22） and ORF1ab（30. 83±6. 27） detected by Q5 analyzer were statistically significant（t=2. 765, P＜0. 05）, while the differences in CT values of N gene（29. 58±7. 27） and ORF1ab（30. 77±8. 02） detected by Bole analyzer were not statistically significant（t=1. 753, P＞0. 05）. The correlation coefficients of CT values of different target genes detected by the two instruments were rN=0. 960 and rORF=0. 865, showing correlated CT values（P＜0. 05）. Conclusion The CT values of N gene and ORF1ab fragment of SARS-CoV-2 virus detected by the two instruments have strong correlation and agreement, indicating that either of the instrument can be used for laboratory sample detection and analysis. The repeatability of Q5 analyzer is better than that of Bole analyzer. The detection stability of ORF fragments of both instruments is better than that of N gene, and the detection sensitivity of Q5 analyzer for N gene is higher than that for ORF fragment. The sample tubes should be placed in the middle of the PCR machine in order to reduce the system error.

Do students look for information differently? Information-seeking behavior during the COVID-19 pandemic

PurposeThe purpose of this study is to explore the COVID-19 information-seeking behavior of the students in a developing country. This study also explores how the use of information sources changes over time by the students of a public university in Bangladesh.Design/methodology/approachAn e-mail was sent along with an online questionnaire to 350 students in a public university in Bangladesh. After sending a couple of follow-up e-mails in May and June 2022, we got limited responses. Later in July, we distributed the same questionnaire in the printed form to the students in the seminar library, computer laboratory and in the classroom. Finally, we got back 270 responses, and the response rate was 77.14%. Pearson's correlation coefficient (effect size) and nonparametric test (Mann–Whitney U test) were used to see the differences in using information sources over times and overall understanding of choosing the COVID-19 information sources by the demographic variables.FindingsThis study found that the COVID-19 pandemic has made an increased demand for a variety of information, and the sources of information changes over time before and during the COVID-19 pandemic. The majority of the students faced challenges while seeking COVID-19 information which mostly falls under the availability of mis–disinformation. Students used more social media tools during the COVID-19 than the pre-COVID-19 time, and there are some significant relationships found between the students' demographic variables and students' understanding of choosing the COVID-19 information sources.Originality/valueTo the best of the authors' knowledge, this study is one of the first to analyze changes in information behavior patterns of students in a developing country and understand the challenges faced by the students during the pandemic.

The Influence of Academic Self-Efficacy on University Students' Academic Performance: The Mediating Effect of Academic Engagement

Challenges and competition are being faced in higher education. Students' unsatisfactory academic performance and dropouts are obvious problems worldwide. The "student-centered” pedagogy requires universities to pay attention to the needs of students. Research has demonstrated that academic self-efficacy is a positive psychological variable in the prevention of students becoming academically burnt out and withdrawing from their studies. By increasing academic engagement and improving academic performance, academic self-efficacy can reduce the dropout rates. This study attempted to achieve an in-depth comprehension of the nexus between academic self-efficacy and academic achievement among university students and the mediating role of academic engagement in the association between the two. A total of 258 participants were included in the cross-sectional study. The relationships among academic self-efficacy, academic engagement, and academic performance were examined using Pearson correlation coefficients. In order to examine the intermediating role of academic engagement in the relationship between academic self-efficacy and academic performance, a mediation analysis was applied. A favorable and strong correlation among academic self-efficacy, academic engagement, and academic performance was found in this study. Academic self-efficacy can be a direct predictor of academic achievement and can also be an indirect predictor of academic achievement via the intermediating effect of academic engagement. The findings of this study provide theoretical and practical recommendations for university researchers and administrators. The findings confirm the mediating role of academic engagement between academic self-efficacy and academic performance. The results provide universities with evidence for use in the design of projects and programs for the improvement of students' academic performance. Increasing the level of academic self-efficacy and enhancing academic engagement are of utmost importance for university students to maintain and improve their academic performance.

Measuring Quality of Life in Residential Aged Care Using the EQ-5D-5L: A Cross-Sectional Study on the Impact of Cognition Level and Proxy Perspective on Interrater Agreement

Quality of life (QoL) is an important outcome in aged care, but self-report is not always possible due to the high prevalence of cognitive impairment in older aged care residents. This study aims to assess the impact of family member proxy perspective (proxy-proxy or proxy-person) on interrater agreement with resident self-report by different cognition levels. The influence of proxy perspective and cognition level is a significant gap in the extant literature which this study seeks to address. A cross-sectional study was undertaken with residents classified into cognition subgroups according to the Mini Mental State Examination. Residents completed the self-report EQ-5D-5L, a well-established generic measure of health-related quality of life (HRQoL). Family member proxies completed EQ-5D-5L proxy version 1 (proxy-proxy perspective, where the proxy responds based on their own opinions) and proxy version 2 (proxy-person perspective, where the proxy responds as they believe the person would). Interrater agreement was assessed using the concordance correlation coefficient (CCC) for utility scores and the weighted kappa for dimension-level responses. Sixty-three residents (n = 22 no cognitive impairment, n = 27 mild impairment, and n = 14 moderate impairment) and proxies participated. EQ-5D-5L utility scores were lower for proxies compared with residents (self-report = 0.522, proxy-proxy = 0.299, and proxy-person = 0.408). Interrater agreement with self-report was higher for proxy-person (CCC = 0.691) than for proxy-proxy (CCC = 0.609). Agreement at the dimension level was higher for more easily observable dimensions, such as mobility, compared to less observable dimensions, such as anxiety/depression. Resident self-reported and proxy family member-reported HRQoL assessments, using the EQ-5D-5L, are different but may be more closely aligned when the proxy is specifically guided to respond from the person's perspective. Further research is needed to address the impact of divergences in self-report and proxy-report ratings of HRQoL for quality assessment and economic evaluation in aged care.

Effect of Transportation Operation on Air Quality in China Based on MODIS AOD during the Epidemic

With the rapid growth of automobile numbers and the increased traffic congestion, traffic has increasingly significant effects on regional air quality and regional sustainable development in China. This study tried to quantify the effect of transportation operation on regional air quality based on MODIS AOD. This paper analyzed the space-time characteristics of air quality and traffic during the epidemic by series analysis and kernel density analysis, and quantified the relationship between air quality and traffic through a Geographically Weighted Regression (GWR) model. The main research conclusions are as follows: The epidemic has a great impact on traffic and regional air quality. PM2.5 and NO2 had the same trend with traffic congestion delay index (CDI), but they were not as obvious as CDI. Both cities with traffic congestion and cities with the worst air quality showed strong spatial dependence. The concentration areas of high AOD value in the east areas of the Hu line were consistent with the two gathering centers formed by cities with traffic congestion in space, and also consistent with the gathering center of cities with poor air quality. The concentration area of AOD decline was consistent with the gathering center formed by cities with the worst air quality. AOD had a strong positive correlation with road network density, and its GWR correlation coefficient was 0.68, then These provinces suitable for GWR or not suitable were divided. This study has a great significance for the transportation planning, regional planning, air quality control strategies and regional sustainable development, etc.

Investigating Bluetooth Low Energy Advertisements in Urban Public Space for Street-level Crowd Density Sensing

Technologies for sensing crowd density have a potential to make our society smarter, and such technologies have been used to help social distancing in the context of COVID-19 pandemic. We have developed a method to sense and forecast street-level crowd density by observing public Bluetooth Low Energy (BLE) advertisements from popular contact tracing applications in Japan. We have deployed our methods in several locations in Tokyo and published the estimated street-level crowd density level on our website as well as a television program. In this paper, we report the status of our project, focusing on the result of experiments to verify the potential of our method after the contact tracing applications stop working. Through an experiment in an urban public space in Tokyo, we have shown that BLE advertisements are almost occupied with contact tracing applications and manufacture specific data from a few companies. In addition, by monitoring different types of BLE advertisements in several locations in Japan, we have clarified that those containing manufacture specific data with a certain company identifier have almost the same trend as those from contact tracing applications, with the average correlation coefficient of 0.990. © 2022 TRON Forum.

Research on Chinese Stock Market during COVID-19—Based on Random Matrix Theory

This paper focuses on the three industries that are greatly impacted by COVID-19, including the consumption industry, the pharmaceutical industry, and the financial industry. The daily returns of 98 stocks in the consumption industry, the pharmaceutical industry, and the financial industry in the 100 trading days from January 2, 2020, to June 3, 2020, are selected. Based on the random matrix theory, it first analyzes whether the stock market conforms to the efficient market hypothesis during the epidemic period, and second it further studies the linkage between the three industries. The results show that (1) the correlation coefficient is approximately a normal distribution, but the mean value is greater than 0, which is greater than that of the more mature markets such as the United States. (2) There are three eigenvalues greater than the prediction value, of which the maximum eigenvalue is about 11.18 times larger than the largest eigenvalue of the RMT. (3) There is a significant positive relationship between the maximum eigenvalue and the correlation coefficient. The specific market performance is that the stock price fluctuations show a high degree of consistency. (4) In the sample interval, the financial industry has a restraining effect on the consumption industry in the short term, and the pharmaceutical industry has a promoting and then restraining effect on the consumption industry in the short term. The consumption industry has a promoting effect on the financial industry in the short term, and the pharmaceutical industry has a promoting and then restraining effect on the financial industry in the short term. The consumption industry has a promoting and then restraining effect on the pharmaceutical industry in the short term, and the financial industry has a promoting and then restraining effect on the pharmaceutical industry in the short term. (5) In the sample interval, the consumption industry is mainly affected by itself, while the role of the pharmaceutical industry and the financial industry is very small. The pharmaceutical industry is mainly affected by itself and the consumption industry, while the role of the financial industry is very small. The financial industry is mainly affected by itself and the consumption industry, while the role of the pharmaceutical industry is very small. This situation has consequences for individual investors and institutional investors, since some stock returns can be expected, creating opportunities for arbitrage and for abnormal returns, contrary to the assumptions of random walk and information efficiency. The research on the correlation between asset returns will help to accurately price assets and avoid losses caused by price fluctuations during the epidemic.

Incidence of incidental positron emission tomography and computed tomography signs of COVID-19 in asymptomatic cancer patients in Egypt

Contexte : La COVID-19 a été signalée pour la première fois en Égypte le 14 février 2020 et demeure une menace majeure pour la santé publique. Objectifs : Nous avons étudié l'incidence des signes fortuits de COVID-19 détectés au moyen de la tomographie par émission de positons/de la tomodensitométrie (TEP/TDM) chez des patients asymptomatiques atteints de cancer. Puis, nous avons comparé cette incidence au nombre de cas de COVID-19 notifiés pendant la même période. Méthodes : Nous avons inclus tous les patients atteints de cancer qui ont subi une TEP/TDM au Misr Radiology Center, au Caire, entre le 2 mai et le 7 août 2020. Résultats : Au total, 479 patients ont subi une TEP/TDM principalement à des fins de suivi, et 66 (13,78 %) d'entre eux ont présenté des signes radiologiques de COVID-19, avec un pic d'incidence au cours des semaines sept et huit de l'étude. Cela a coïncidé et était fortement corrélé avec le pic d'incidence de la COVID-19 en Égypte (test du coefficient de corrélation de Pearson = 0,943). Conclusion : L'incidence des signes fortuits de COVID-19 détectés par TEP/TDM était conforme à l'incidence officiellement notifiée de la COVID-19 en Égypte entre le 2 mai et le 7 août 2020. Ces résultats pourraient être utiles pour mettre en œuvre et ajuster les mesures sociales et de santé publique durant la pandémie de COVID-19.Alternate :Background: COVID-19 was first reported in Egypt on 14 February 2020 and continues to be a major threat to public health. Aims: We studied the incidence of incidental positron emission tomography/computed tomography (PET/CT) signs of COVID-19 in asymptomatic cancer patients and compared this with the number of reported COVID-19 cases during the same period. Methods: We included all cancer patients who underwent PET/CT at Misr Radiology Center, Cairo, between 2 May and 7 August 2020. Results: There were 479 patients who underwent PET/CT primarily for follow-up, and 66 (13.78%) of them showed radiological signs of COVID-19, with the peak incidence in weeks 7-8 of the study. This coincided and strongly correlated with the peak incidence of COVID-19 in Egypt (Pearson's correlation coefficient test = 0.943). Conclusion: The incidence of incidental PET/CT signs of COVID-19 was in accordance with the officially reported incidence of COVID-19 in Egypt between 2 May and 7 August 2020. These results could be helpful for implementing and adjusting public health and social measures during the COVID-19 pandemic.

Correlation coefficients for T-spherical fuzzy sets and their applications in pattern analysis and multi-attribute decision-making

T-spherical fuzzy set is an effective tool to deal with vagueness and uncertainty in real-life problems, especially in a situation when there are more than two circumstances, like in casting a ballot. The correlation coefficient of T-spherical fuzzy sets is a tool to calculate the association of two T-spherical fuzzy sets. It has several applications in various disciplines like science, management, and engineering. The noticeable applications incorporate pattern analysis, decision-making, medical diagnosis, and clustering. The aim of this article is to introduce some correlation coefficients for T-spherical fuzzy sets with their applications in pattern recognition and decision-making. The under discussion correlation coefficients are far more advantageous than the existing and many other tools used for T-spherical fuzzy sets, because they are used completely and demonstrate the nature as well as the limit of association between two T-spherical fuzzy sets. Further, an application of proposed correlation coefficients in pattern analysis is discussed here. In addition to it, the proposed correlation coefficients are applied to a multi-attribute decision-making problem, in which the selection of a suitable COVID-19 mask is presented. A comparative analysis has also been made to check the effectiveness of the proposed work with the existing correlation coefficients.

Influence of Pedagogy on the Epistemological Estimation of Electrical Engineering Subject

COVID-19 pandemic has brought sudden changes in teaching and learning process compared to conventional face to face mode of education all around the globe. During social distancing in pandemic environment, the most common change that has been introduced was to opt online and hybrid mode of learning using e-resources by students and faculty at several organizations. In our organization, the BlackboardTM platform has been used to teach the course either in e-learning or blended mode. The present work is an epistemic case study of an electrical engineering subject taught in blended mode to undergraduate students. The performances of the students have been analysed in continuous assessment as well as in final assignment. The analysis criteria was based on expected “course learning outcomes” taken from ABET guidelines which was planned before the commencement of academic semester. In this case study, a specific part of the final assignment in which a questionnaire was framed to assess the “Understanding”, “Apply”, “Analyzed”, “Evaluate” and “Create” levels of Bloom's Taxonomy by determining the correlation factor among various parameters. It was observed that students had felt the difficulty in achieving the satisfactory response in “Evaluate” and “Create” while performed well in first three levels of Bloom's Taxonomy. Based on analysis and results, it is concluded that to achieve satisfactory response of the students, continuous hands-on-experience of laboratory experiments and instruments are essential. In the coming months when it is difficult to start the face to face mode of teaching and learning, an alternate method for laboratory could be catered by introducing virtual laboratory and simulations. In addition a remedial plan has to be prepared to enhance the critical thinking of the students to improve the “Evaluate” and “Create” levels of students. © 2022, Rajarambapu Institute Of Technology. All rights reserved.

Statistical Analysis of the Impact of COVID-19 on PM2.5 Concentrations in Downtown Quito during the Lockdowns in 2020.

In this paper, a comparative analysis between the PM2.5 concentration in downtown Quito, Ecuador, during the COVID-19 pandemic in 2020 and the previous five years (from 2015 to 2019) was carried out. Here, in order to fill in the missing data and achieve homogeneity, eight datasets were constructed, and 35 different estimates were used together with six interpolation methods to put in the estimated value of the missing data. Additionally, the quality of the estimations was verified by using the sum of squared residuals and the following correlation coefficients: Pearson's r, Kendall's τ, and Spearman's ρ. Next, feature vectors were constructed from the data under study using the wavelet transform, and the differences between feature vectors were studied by using principal component analysis and multidimensional scaling. Finally, a robust method to impute missing data in time series and characterize objects is presented. This method was used to support the hypothesis that there were significant differences between the PM2.5 concentration in downtown Quito in 2020 and 2015-2019.

Digital Future of Emergency Medical Services: Envisioning and Usability of Electronic Patient Care Report System

Despite the efforts of emerging technologies in the healthcare system, there is still a slower rate of acceleration in prehospital settings compared with the hospitals in digital transformation adaptation. The acknowledgment that digital transformation is significant to healthcare is reflected in planning for the future of digital healthcare. Thus, this study aimed to measure the usability of the electronic patient care report (ePCR) system among emergency medical services (EMS) staff who work in prehospital settings. A descriptive cross-sectional correlation study was used. Two hundred fifty EMS staff who are working in the prehospital setting at Saudi Red Crescent Authority in the Kingdom of Saudi Arabia were surveyed, and the response rate was 79.2% (198). An adapted tool of the Computer System Usability Questionnaire survey was used to collect data. The data were coded numerically and subjected to descriptive and inferential statistical analysis including Pearson’s correlation coefficient using the statistical software (SPSS 21). The majority of the participants rate their ePCR system as “useable” at a high level with a score of 3.41 (SD = 1.021). The overall mean of the ePCR system’s three subscales: system usefulness, information quality, interface quality, and overall satisfaction were 3.39 (SD = 1.152), 3.30 (SD = 1.052), 3.57 (SD = 1.064), and 3.37 (SD = 1.239), respectively. The least liked aspect of ePCR system software was information quality 81 (40.9%). Furthermore, there was a significant correlation between the age of EMS staff and the usability of the ePCR system (r = −0.150 ∗, P=0.035). The results suggest that healthcare institutions’ policy and decision-makers pay close attention to performing standardized training for the staff on their ePCR system before going to the field to increase efficiency and productivity. Furthermore, the users in this study identified other system features that, if included, could have enhanced usability, and improved functions and capabilities of the design to meet the EMS staff’s expectations.

Exploring Anxiety of Québec University Community during COVID-19 Pandemic via Machine Learning

Many studies showed that COVID-19 global pandemic had a negative impact on the mental health of post-secondary students over the world. To date, very few studies have been conducted in a university setting, not only with students but also with employees. Moreover, almost all studies were based on classical statistical analysis. In this study, we investigated the level of anxiety felt by the Quebec university community (students and employees) during COVID-19 pandemic. Especially, we focused on the generalized anxiety disorder (GAD-7) score with the help of classical data exploration and predictive machine learning techniques. We observed that the best predictive model of the GAD-7 score was provided by the CatBoost algorithm) reaching a squared Pearson correlation coefficient of r2 = 0.5656. Moreover, we also explored variable importance and interaction effects between variables involved in the predictive model obtained using SHapley Additive exPlanations (SHAP). © 2022 ACM.

Wireless Communication System and Its Application in Big Data Remote Monitoring and Decision-Making

The development of computer technology has promoted the widespread application of unmanned technology. Remote monitoring of wireless devices is an application of unmanned technology. To improve the remote monitoring of wireless devices, this study establishes a remote monitoring and decision-making framework based on wireless communication systems. With the wireless communication system, signals that characterize the operating status of devices can be obtained in real-time. Based on the collected signals, the remote monitoring system can identify the current health status of wireless devices, thereby providing auxiliary decision-making for device operation. In the case study, the main engine of an unmanned surface vehicle is used as the study object. The results show that most of the relative errors corresponding to the state identification results of the established remote monitoring framework are within 5%. Moreover, the results present that the linear correlation coefficients between the predicted and real results are greater than 0.95. Therefore, the established remote monitoring framework based on the wireless communication system has good reliability in the state identification of wireless devices.

Competences of Rural Women Entrepreneurs and Their Quality of Life

Background: Women entrepreneurs, especially those from the rural areas, often struggle to develop balance between business decisions and their well-being. Objective: To examine the relationship between rural women entrepreneurs’ competence and their quality of life. Methods: A questionnaire survey measuring life competencies and the quality of life was carried out on a group of 152 women entrepreneurs from rural Perak using the purposive sampling technique. The main research method was quantitative using survey design. The collected data were subjected to statistical analysis using frequency, mean, standard deviation and correlation coefficient were used to assess the relationship between entrepreneurial competence and quality of life. Results: Findings showed that rural women entrepreneurs have a higher level of life competencies and achieved a good quality of life. There is a strong relationship between their life competencies and quality of life and similarly, between rural women entrepreneurs’ entrepreneurial skills and spiritual skills and their quality of life. Implication: The applicability of Maslow’s hierarchy of needs and Spencer and Spencer’s theory is highly proven by the evidence of a relationship between life competencies and rural women entrepreneur’s quality of life. These findings have implications for enhancing the efficiency of rural women entrepreneurs through the implementation of competency development programs.

Study on the Relationship between Restoration Benefit and Visual Satisfaction of LONG-PLAN’s Indoor Vertical Greenery

This study investigates the psychological restorative benefits of indoor vertical greenery and its relationship with visual satisfaction. Taking the Solar Decathlon China 2018 champion project “LONG-PLAN” as the experimental field, we conducted a questionnaire survey to evaluate the effect of indoor vertical greenery on creating a restorative environment. Then we further studied the relationship between the restorative environmental factors and visual satisfaction of indoor vertical greenery. The results show that: (1) Indoor vertical greenery has a positive impact on the subjective restoration of respondents (the average value of PRS = 4.150). (2) The three factors of “being away,” “fascination and compatibility,” and the “extent” of environmental restoration have a significant positive correlation with the visual satisfaction of indoor vertical greenery (the correlation coefficient values are 0.403, 0.627, and 0.425, respectively, p < 0.01). (3) In the stepwise regression analysis of the three factors and the visual satisfaction of indoor vertical greenery, only “fascination and compatibility” show a significant positive correlation (the regression coefficient = 0.753, p < 0.01). (4) The visual satisfaction of indoor vertical greenery has a significantly positive impact on environmental recovery (the regression coefficient = 0.459, p < 0.01). The study shows that indoor vertical greenery improves visual satisfaction and contributes to a restorative environment. In addition, the study provides further evidence of the mutual facilitation between restorative benefits and visual satisfaction.

COVID-19 Symptom Analysis and Prediction Using Machine Learning Techniques

The globe is still in a state of panic as the epidemic of COVID-19 continues to spread. Vaccines have been introduced all across the world, but many people continue to be affected. As a result, knowing former patients’ medical information may benefit medics in their battle against the disease. Artificial Intelligence (AI) has developed as a groundbreaking tool with capabilities such as meteorology, forecasts in the medical sector, predictive analytics, and so on. One of the most prominent areas of AI is Machine Learning (ML) that has recently shown promising results in a variety of fields, including medicine and, most recently, COVID analysis. In this paper, we have performed two works with regard to COVID crisis. Firstly, we have conducted a study on the most impacting symptoms noticed in a person with COVID by applying a correlation analysis with Pearson and Spearman correlation coefficients. Second, the COVID dataset was analyzed using six machine learning methods for classification tasks: Random Forest (RF), Gradient Boosting, Decision Trees (DTs), Naïve Bayes (NB), Bernoulli Naïve Bayes, and Support Vector Machine (SVM), with the most influential symptoms as inputs. The performance of these algorithms are measured using the metrics, namely accuracy, F1-score, precision score, and Area under the Receiver Operating Characteristic Curve (ROC-AUC) score. On evaluating the applied machine learning algorithms, it can be concluded that all the six algorithms were found to be efficient in distinguishing the positive and negative cases of COVID with promising values of the performance metrics. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Swin Transformer for COVID-19 Infection Percentage Estimation from CT-Scans

Coronavirus disease 2019 (COVID-19) is an infectious disease that has spread globally, disrupting the health care system and claiming millions of lives worldwide. Because of the high number of Covid-19 infections, it has been challenging for medical professionals to manage this crisis. Estimating the Covid-19 percentage can help medical staff categorize patients by severity and prioritize accordingly. With this approach, the intensive care unit (ICU) can free up resuscitation beds for the critical cases and provide other treatments for less severe cases to efficiently manage the healthcare system during a crisis. In this paper, we present a transformer-based method to estimate covid-19 infection percentage for monitoring the evolution of the patient state from computed tomography scans (CT-scans). We used a particular Transformer architecture called Swin Transformer as a backbone network to extract the feature from the CT slice and pass it through multi-layer perceptron (MLP) to obtain covid-19 infection percentage. We evaluated our approach on the covid-19 infection percentage estimation challenge dataset, annotated by two expert radiologists. The experimental results show that the proposed method achieves promising performance with a mean absolute error (MAE) of 4.5042, Pearson correlation coefficient (PC) of 0.9490, root mean square error (RMSE) of 8.0964 on the given Val set leaderboard and a MAE of 3.5569, PC of 0.8547 and RMSE of 7.5102 on the given Test set Leaderboard. These promising results demonstrate the high potential of Swin Transformer architecture for this image regression task of covid-19 infection percentage estimation from CT-scans. The source code of this project can be found at: https://github.com/suman560/Covid-19-infection-percentage-estimation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.